Implementation of Kalman filter with multicore system on chip using function — Level parallelism

Kalman filter is a very popular estimation technique used widely for linear tracking. It uses a set of noisy data as input and produces state estimates with minimum error rate. This study aims to explore how to implement implicit parallelism in multi-core processor and object tracking with task-level parallelism and Kalman Filter is parallelized on Multi-core system on chip. The novelty of this study is the introduction of Adaptive Load Balancing Approach (ALBA) to compute the nonrecursive algorithm. This approach can be applied on all form of multicore computers. The parallel Kalman Filter is developed in C# for multicore using .Net framework 4.0. It uses combination of C and CUDA for its implementation on GPU.